Energies 2014, 7(1), 191-209; doi:10.3390/en7010191
Article

Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems

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Received: 17 September 2013; in revised form: 27 November 2013 / Accepted: 31 December 2013 / Published: 7 January 2014
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Abstract: A strategy was proposed to determine the optimal operating point for the proportional-integral-derivative (PID) controller of a wind turbine, and identify the stability regions in the parameter space. The proposed approach combined particle swarm optimization (PSO) and radial basis function neural network (RBFNN) algorithms. These intelligent algorithms are artificial learning mechanisms that can determine the optimal operating points, and were used to generate the function representing the most favorable operating  parameters from each parameter of  for the stability region of the PID controller. A graphical method was used to determine the 2D or 3D vision boundaries of the PID-type controller space in closed-loop wind turbine systems. The proposed techniques were demonstrated using simulations of a drive train model without time delay and a pitch control model with time delay. Finally, the 3D stability boundaries were determined the proposed graphical approach with and without time delay systems.
Keywords: wind turbine; proportional-integral-derivative (PID) control; particle swarm optimization (PSO); radial basis function (RBF); stability boundary; time delay
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MDPI and ACS Style

Perng, J.-W.; Chen, G.-Y.; Hsieh, S.-C. Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems. Energies 2014, 7, 191-209.

AMA Style

Perng J-W, Chen G-Y, Hsieh S-C. Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems. Energies. 2014; 7(1):191-209.

Chicago/Turabian Style

Perng, Jau-Woei; Chen, Guan-Yan; Hsieh, Shan-Chang. 2014. "Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems." Energies 7, no. 1: 191-209.

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